# devtools::install_local("geneRefineR/", force = T)
# library("echolocatoR") 
source("echolocatoR/R/functions.R") 

library(readxl)
library(DT)
library(data.table)
library(dplyr)
library(ggplot2)
library(plotly)
library(cowplot)
library(ggrepel)
library(curl)
library(biomaRt) 
# Ensembl LD API
library(httr)
library(jsonlite)
library(xml2)
library(gaston)
library(RCurl)
# *** susieR ****
# library(knitrBootstrap) #install_github('jimhester/knitrBootstrap')
library(susieR) # devtools::install_github("stephenslab/susieR")

sessionInfo()

## R version 3.5.1 (2018-07-02)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS release 6.9 (Final)
## 
## Matrix products: default
## BLAS/LAPACK: /hpc/packages/minerva-common/intel/parallel_studio_xe_2018/compilers_and_libraries_2018.1.163/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] susieR_0.6.2.0411  tidyr_0.8.1        RCurl_1.95-4.11   
##  [4] bitops_1.0-6       gaston_1.5.4       RcppParallel_4.4.2
##  [7] Rcpp_1.0.0         xml2_1.2.0         jsonlite_1.5      
## [10] httr_1.3.1         biomaRt_2.38.0     curl_3.2          
## [13] ggrepel_0.8.0      cowplot_0.9.3      plotly_4.7.1      
## [16] ggplot2_3.1.0      dplyr_0.7.6        data.table_1.11.8 
## [19] DT_0.4             readxl_1.1.0      
## 
## loaded via a namespace (and not attached):
##  [1] lattice_0.20-35      prettyunits_1.0.2    assertthat_0.2.0    
##  [4] rprojroot_1.3-2      digest_0.6.18        R6_2.4.0            
##  [7] cellranger_1.1.0     plyr_1.8.4           backports_1.1.2     
## [10] stats4_3.5.1         RSQLite_2.1.1        evaluate_0.11       
## [13] pillar_1.3.1         rlang_0.3.1          progress_1.2.0      
## [16] lazyeval_0.2.1       blob_1.1.1           S4Vectors_0.20.1    
## [19] Matrix_1.2-14        rmarkdown_1.10       stringr_1.4.0       
## [22] htmlwidgets_1.2      bit_1.1-14           munsell_0.5.0       
## [25] compiler_3.5.1       pkgconfig_2.0.2      BiocGenerics_0.28.0 
## [28] htmltools_0.3.6      tidyselect_0.2.4     expm_0.999-2        
## [31] tibble_2.0.1         IRanges_2.16.0       XML_3.98-1.12       
## [34] viridisLite_0.3.0    crayon_1.3.4         withr_2.1.2         
## [37] grid_3.5.1           gtable_0.2.0         DBI_1.0.0           
## [40] magrittr_1.5         scales_1.0.0         stringi_1.3.1       
## [43] bindrcpp_0.2.2       tools_3.5.1          bit64_0.9-7         
## [46] Biobase_2.42.0       glue_1.3.0           purrr_0.2.5         
## [49] hms_0.4.2            parallel_3.5.1       yaml_2.1.19         
## [52] AnnotationDbi_1.44.0 colorspace_1.3-2     memoise_1.1.0       
## [55] knitr_1.20           bindr_0.1.1

print(paste("susieR ", packageVersion("susieR")))

## [1] "susieR  0.6.2.411"

# MINERVA PATHS TO SUMMARY STATISTICS DATASETS:
# root <- "../../.."
root <- "~/../../../sc/orga/projects"
Data_dirs = list(
  # ++++++++ GWAS SUMMARY STATS ++++++++ # 
  # Nall et al. (2019) w/ 23andMe
  "Nalls_23andMe" = list(type="Parkinson's GWAS", 
                         topSS="Data/Parkinsons/Nalls_23andMe/Nalls2019_TableS2.xlsx",
                   # fullSS=file.path(root,"pd-omics/data/nallsEtAl2019/23andme/PD_all_post30APRIL2015_5.2_extended.txt")),
                   fullSS=file.path(root,"pd-omics/data/nallsEtAl2019/combined_meta/nallsEtAl2019_allSamples_allVariants.mod.txt"),
                   reference="https://www.biorxiv.org/content/10.1101/388165v3"),  
  
  ## IGAP 
  "Lambert_2013" = list(type="Alzheimer's GWAS", 
                topSS="Data/Alzheimers/Lambert_2013/Lambert_2019_AD_GWAS.xlsx",
                fullSS=file.path(root,"ad-omics/data/AD_GWAS/Lambert_2013/IGAP_stage_1.1000G.phase3.20130502.tsv"),
                reference="https://www.nature.com/articles/ng.2802"),
  
  ## Marioni et al. (2018) 
  "Marioni_2018" = list(type="Alzheimer's GWAS", 
                        topSS="Data/Alzheimers/Marioni_2018/Marioni2018_supplementary_tables.xlsm",
                        fullSS=file.path(root,"ad-omics/data/AD_GWAS/Marioni_2018/Marioni2018.4_UK_Biobank_IGAP_17May2018.1000G.phase3.20130502.tsv"),
                        reference="https://www.nature.com/articles/s41398-018-0150-6"),
  
  ## Jansen et al. (2018) 
  "Posthuma_2018" = list(type="Alzheimer's GWAS", 
                         topSS="Data/Alzheimers/Posthuma_2018/Posthuma2018_suppTables.xlsx", 
                         fullSS=file.path(root,"ad-omics/data/AD_GWAS/Posthuma_2018/phase3.beta.se.hrc.txt"),
                reference="https://www.nature.com/articles/s41588-018-0311-9"),
  
  ## Kunkle et al. (2018) Alzheimer's GWAS 
  "Kunkle_2019" = list(type="Alzheimer's GWAS",
                       topSS="Data/Alzheimers/Kunkle_2019/Kunkle2019_supplementary_tables.xlsx", 
                       fullSS=file.path(root,"ad-omics/data/AD_GWAS/Kunkle_2019/Kunkle_etal_Stage1_results.txt"),
                       # fullSS_stage2=file.path(root,"ad-omics/data/AD_GWAS/Kunkle_etal_Stage2_results.txt"),
                       reference="https://www.nature.com/articles/s41588-019-0358-2"),

  # ++++++++ eQTL SUMMARY STATS ++++++++ #
  ## MESA eQTLs: African Americans
  "MESA_AFA" = list(type="eQTL",
                    topSS="Data/eQTL/MESA/AFA_eQTL_PTK2B.txt",
                    fullSS=file.path(root,"ad-omics/data/mesa/AFA_cis_eqtl_summary_statistics.txt"), 
                    reference="https://www.nhlbi.nih.gov/science/multi-ethnic-study-atherosclerosis-mesa"),
  ## MESA eQTLs: Caucasians
  "MESA_CAU" = list(type="eQTL",
                    topSS="Data/eQTL/MESA/CAU_eQTL_PTK2B.txt",
                    fullSS=file.path(root,"ad-omics/data/mesa/CAU_cis_eqtl_summary_statistics.txt"),
                    reference="https://www.nhlbi.nih.gov/science/multi-ethnic-study-atherosclerosis-mesa"),
  ## MESA eQTLs: Hispanics
  "MESA_HIS" = list(type="eQTL",
                    topSS="Data/eQTL/MESA/HIS_eQTL_PTK2B.txt",
                    fullSS=file.path(root,"ad-omics/data/mesa/HIS_cis_eqtl_summary_statistics.txt"),
                    reference="https://www.nhlbi.nih.gov/science/multi-ethnic-study-atherosclerosis-mesa") 
  )

Data_dirs_table <- Data_dirs_to_table(Data_dirs, writeCSV = "Results/directories_table.csv")

# Direct to local data files if not working on Minerva
if(getwd()=="/Users/schilder/Desktop/Fine_Mapping"){
  vcf_folder = F # Use internet if I'm working from my laptop
} else{
  vcf_folder = F#"../1000_Genomes_VCFs/Phase1" # Use previously downloaded files if working on Minerva node
}  

force_new_subset = F
allResults <- list()

Parkinson’s Disease

Nalls et. al. (2019) w/ 23andMe

“Rare coding variant burden analysis:
We performed kernel-based burden tests on the 113 genes in our PD GWAS loci that contained two or more rare coding variants (MAF< 5% or MAF < 1%). After Bonferroni correction for 113 genes, we identified 7 significant putatively causal genes: - LRRK2, GBA, CATSPER3 (rs11950533/C5orf24 locus) - LAMB2 (rs12497850/IP6K2 locus) - LOC442028 (rs2042477/KCNIP3 locus) - NFKB2 (rs10748818/GBF1 locus), and SCARB2 (rs6825004 locus).”
– from Nalls et al. (2019)

dataset_name <- "Nalls_23andMe"

top_SNPs <- import_topSNPs(
  file_path = Data_dirs[[dataset_name]]$topSS,
  chrom_col = "CHR", position_col = "BP", snp_col="SNP",
  pval_col="P, all studies", effect_col="Beta, all studies", gene_col="Nearest Gene",
  caption= "Nalls et al. (2018) w/ 23andMe PD GWAS Summary Stats")

gene_list <- c("LRRK2","SNCA","VPS13C","C5orf24","IP6K2","KCNIP3","GBF1","SCARB2")#, top_SNPs$Gene) %>% unique()
 
allResults[[dataset_name]] <- finemap_geneList(top_SNPs,
                                               geneList=gene_list,
                 file_path= Data_dirs[[dataset_name]]$fullSS,#"./LRRK2_EUR_subset.txt",#
                 chrom_col = "CHR", position_col = "POS", snp_col = "RSID",
                 pval_col = "p", effect_col = "beta", stderr_col = "se", 
                 vcf_folder = vcf_folder, superpopulation = "EUR", force_new_subset = force_new_subset)

LRRK2

Extracting SNPs flanking lead SNP… —Min snp position: 40234202 — —Max snp position: 41234202 — Subset file already exists. Importing /hpc/users/schilb03/../../../sc/orga/projects/pd-omics/data/nallsEtAl2019/combined_meta/LRRK2_combined_meta_subset.txt …
Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/LRRK2_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-4819.31771667441” [1] “objective:-4817.53694272212” [1] “objective:-4817.5346354578” [1] “objective:-4817.53463247054” [1] “objective:-4817.5346324668” [1] “objective:-4817.53463246679”

Credible Set:

****** 5 SNPs included in Credible Set ******

## Warning: Removed 1 rows containing missing values (geom_hline).

SNCA

Extracting SNPs flanking lead SNP… —Min snp position: 90126111 — —Max snp position: 91126111 — Subset file already exists. Importing /hpc/users/schilb03/../../../sc/orga/projects/pd-omics/data/nallsEtAl2019/combined_meta/SNCA_combined_meta_subset.txt …
Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/SNCA_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-3916.13477060794” [1] “objective:-3885.12408928578” [1] “objective:-3885.10414431053” [1] “objective:-3885.10413226354” [1] “objective:-3885.10413225772” [1] “objective:-3885.10413225772”

Credible Set:

****** 1 SNPs included in Credible Set ******

## Warning: Removed 1 rows containing missing values (geom_hline).

VPS13C

Extracting SNPs flanking lead SNP… —Min snp position: 61497385 — —Max snp position: 62497385 — Subset file already exists. Importing /hpc/users/schilb03/../../../sc/orga/projects/pd-omics/data/nallsEtAl2019/combined_meta/VPS13C_combined_meta_subset.txt …
Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/VPS13C_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-3963.78730870254” [1] “objective:-3963.32612301913” [1] “objective:-3963.3257570866” [1] “objective:-3963.32575679555”

Credible Set:

****** 8 SNPs included in Credible Set ******

## Warning: Removed 1 rows containing missing values (geom_hline).

C5orf24

Extracting SNPs flanking lead SNP… —Min snp position: 133699105 — —Max snp position: 134699105 — Subset file already exists. Importing /hpc/users/schilb03/../../../sc/orga/projects/pd-omics/data/nallsEtAl2019/combined_meta/C5orf24_combined_meta_subset.txt …
Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/C5orf24_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-2730.39327779333” [1] “objective:-2730.27173484353” [1] “objective:-2730.27159727042” [1] “objective:-2730.27159711459”

Credible Set:

****** 37 SNPs included in Credible Set ******

## Warning: Removed 1 rows containing missing values (geom_hline).

IP6K2

Extracting SNPs flanking lead SNP… —Min snp position: 48248989 — —Max snp position: 49248989 — Subset file already exists. Importing /hpc/users/schilb03/../../../sc/orga/projects/pd-omics/data/nallsEtAl2019/combined_meta/IP6K2_combined_meta_subset.txt …
Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/IP6K2_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-1732.44890838796” [1] “objective:-1732.18758589222” [1] “objective:-1732.18663923239” [1] “objective:-1732.18663574514”

Credible Set:

— ERROR — ****** Could NOT identify credible set. Default to SNPs with the top 5 PIPs ******

## Warning: Removed 1 rows containing missing values (geom_hline).

KCNIP3

Extracting SNPs flanking lead SNP… —Min snp position: 95500943 — —Max snp position: 96500943 — Subset file already exists. Importing /hpc/users/schilb03/../../../sc/orga/projects/pd-omics/data/nallsEtAl2019/combined_meta/KCNIP3_combined_meta_subset.txt …
Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/KCNIP3_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-1045.5376558212” [1] “objective:-1045.2548831746” [1] “objective:-1045.25378394202” [1] “objective:-1045.25377968046” [1] “objective:-1045.25377966438” [1] “objective:-1045.25377966432”

Credible Set:

****** 58 SNPs included in Credible Set ******

## Warning: Removed 1 rows containing missing values (geom_hline).

GBF1

Extracting SNPs flanking lead SNP… —Min snp position: 103515279 — —Max snp position: 104515279 — Subset file already exists. Importing /hpc/users/schilb03/../../../sc/orga/projects/pd-omics/data/nallsEtAl2019/combined_meta/GBF1_combined_meta_subset.txt …
Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/GBF1_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-2327.33554736521” [1] “objective:-2327.14720605155” [1] “objective:-2327.14644032417” [1] “objective:-2327.14643727145”

Credible Set:

— ERROR — ****** Could NOT identify credible set. Default to SNPs with the top 5 PIPs ******

## Warning: Removed 1 rows containing missing values (geom_hline).

SCARB2

Extracting SNPs flanking lead SNP… —Min snp position: 76610365 — —Max snp position: 77610365 — Subset file already exists. Importing /hpc/users/schilb03/../../../sc/orga/projects/pd-omics/data/nallsEtAl2019/combined_meta/SCARB2_combined_meta_subset.txt …
Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/SCARB2_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-4438.64199045971” [1] “objective:-4438.05391400102” [1] “objective:-4438.05352828151” [1] “objective:-4438.05352803242”

Credible Set:

****** 4 SNPs included in Credible Set ******

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 207 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 207 rows containing missing values (geom_segment).

## Warning: Removed 4 rows containing missing values (geom_text_repel).

#c("LRRK2","SNCA","VPS13C","GCH1"),
# "GBA","CATSPER3", "LAMB2", "LOC442028", "NFKB2", "SCARB2", "GRN"

Alzheimer’s Disease

Posthuma (2018)

dataset_name <- "Posthuma_2018"

top_SNPs <- import_topSNPs(
  file_path = Data_dirs[[dataset_name]]$topSS,
  sheet = "Table S8",
  chrom_col = "Chr", position_col = "bp", snp_col="SNP",
  pval_col="P", effect_col="Zscore", gene_col="nearestGene",
  caption= "Posthuma et al. (2018) AD GWAS Summary Stats")

allResults[[dataset_name]] <- finemap_geneList(top_SNPs, geneList=c("PTK2B"),
                 file_path=Data_dirs[[dataset_name]]$fullSS,#"Data/Alzheimers/Posthuma_2018/phase3.beta.se.hrc.txt",#
                 effect_col = "BETA", stderr_col = "SE", position_col = "BP",
                 snp_col = "SNP", pval_col = "P",
                 vcf_folder = vcf_folder, superpopulation = "EUR",
                 minPos=NULL, maxPos=27440000, force_new_subset = force_new_subset)

PTK2B

Extracting SNPs flanking lead SNP… —Min snp position: 26695121 — —Max snp position: 27440000 — Subset file already exists. Importing /hpc/users/schilb03/../../../sc/orga/projects/ad-omics/data/AD_GWAS/Posthuma_2018/PTK2B_Posthuma_2018_subset.txt …
Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/PTK2B_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-2946.7022383842” [1] “objective:-2946.45848339141” [1] “objective:-2946.45830481344” [1] “objective:-2946.45830468346”

Credible Set:

****** 6 SNPs included in Credible Set ******

## Warning: Removed 1 rows containing missing values (geom_hline).

Marioni (2018)

dataset_name <- "Marioni_2018"

top_SNPs <- import_topSNPs(
  file_path = Data_dirs[[dataset_name]]$topSS,
  sheet = "Table S9",
  chrom_col = "topSNP_chr", position_col = "topSNP_bp", snp_col="topSNP",
  pval_col="p_GWAS", effect_col="b_GWAS", gene_col="Gene",
  caption= "Marioni et al. (2018) AD GWAS Summary Stats")

allResults[[dataset_name]] <- finemap_geneList(top_SNPs, geneList=c("PTK2B"),
                 file_path=Data_dirs[[dataset_name]]$fullSS,#"Data/Alzheimers/Marioni_2018/Marioni2018.4_UK_Biobank_IGAP_17May2018.1000G.phase3.20130502.tsv", #,
                 effect_col = "BETA", stderr_col = "SE", position_col = "POS", 
                 snp_col = "SNP",chrom_col = "CHROM", pval_col = "PVAL",
                 vcf_folder = vcf_folder, superpopulation = "EUR",
                 minPos=NULL, maxPos=27440000, force_new_subset = force_new_subset)

PTK2B

Extracting SNPs flanking lead SNP… —Min snp position: 26688518 — —Max snp position: 27440000 — Subset file already exists. Importing /hpc/users/schilb03/../../../sc/orga/projects/ad-omics/data/AD_GWAS/Marioni_2018/PTK2B_Marioni_2018_subset.txt …
Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/PTK2B_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-1966.3994025023” [1] “objective:-1965.70884939607” [1] “objective:-1965.70807253082” [1] “objective:-1965.70807165376”

Credible Set:

****** 5 SNPs included in Credible Set ******

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 354 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 354 rows containing missing values (geom_segment).

## Warning: Removed 5 rows containing missing values (geom_text_repel).

Lambert (2013) (IGAP)

Filtering out the CLU portion of the locus prevents susieR from identifying a credible set.

dataset_name <- "Lambert_2013"

top_SNPs <- import_topSNPs(
  file_path = Data_dirs[[dataset_name]]$topSS,
  sheet = 1,
  chrom_col = "CHR", position_col = "Position", snp_col="SNP",
  pval_col="Overall_P", effect_col="Overall_OR", gene_col="Closest gene",
  caption= "Lambert (2013) (IGAP): AD GWAS Summary Stats")
# genes <- snps_to_genes(snp_list=top_SNPs$SNP )

allResults[[dataset_name]] <- finemap_geneList(top_SNPs, geneList=c("PTK2B"), 
                   file_path=Data_dirs[[dataset_name]]$fullSS,#"Data/Alzheimers/Lambert_2013/IGAP_stage_1.1000G.phase3.20130502.tsv",#
                   chrom_col = "CHROM",  pval_col = "PVAL", snp_col = "SNP",
                   effect_col = "BETA", stderr_col = "SE", position_col = "POS", 
                   vcf_folder = vcf_folder, num_causal = 1, superpopulation = "EUR",
                   minPos=NULL, maxPos=27440000, force_new_subset = force_new_subset)

PTK2B

Extracting SNPs flanking lead SNP… —Min snp position: 26695121 — —Max snp position: 27440000 — Subset file already exists. Importing /hpc/users/schilb03/../../../sc/orga/projects/ad-omics/data/AD_GWAS/Lambert_2013/PTK2B_Lambert_2013_subset.txt …
Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/PTK2B_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-1875.33189392468” [1] “objective:-1875.12745540191” [1] “objective:-1875.12685678814” [1] “objective:-1875.12685505843”

Credible Set:

— ERROR — ****** Could NOT identify credible set. Default to SNPs with the top 5 PIPs ******

## Warning: Removed 1 rows containing missing values (geom_hline).

Kunkle (2019) (IGAP)

Exclude CLU-associated portion of locus by removing all SNPs over position 8:27440000.
The CLU-gene technically starts at 8:27456253, but its associated SNPs are further upstream.

dataset_name <- "Kunkle_2019"

top_SNPs <- import_topSNPs(
  file_path = Data_dirs[[dataset_name]]$topSS,
  sheet = "Supplementary Table 6",
  chrom_col = "Chr", position_col = "Basepair", snp_col="SNP",
  pval_col="P", effect_col="Beta", gene_col="LOCUS",
  caption= "Kunkle (2019) (IGAP): AD GWAS Summary Stats")
# genes <- snps_to_genes(snp_list=top_SNPs$SNP )

allResults[[dataset_name]] <- finemap_geneList(top_SNPs, geneList=c("PTK2B"),  
                   file_path=Data_dirs[[dataset_name]]$fullSS,#"Data/Alzheimers/Kunkle_2019/Kunkle_etal_Stage1_results.txt",#
                   chrom_col = "Chromosome",  position_col = "Position", pval_col = "Pvalue",
                   snp_col = "MarkerName", effect_col = "Beta", stderr_col = "SE", 
                   vcf_folder = vcf_folder, num_causal = 1, superpopulation = "EUR",
                   minPos=NULL, maxPos=27440000, file_sep=" ", force_new_subset = force_new_subset) # Exclude CLU-associated portion of locus

PTK2B

Extracting SNPs flanking lead SNP… —Min snp position: 26719987 — —Max snp position: 27440000 — Subset file already exists. Importing /hpc/users/schilb03/../../../sc/orga/projects/ad-omics/data/AD_GWAS/Kunkle_2019/PTK2B_Kunkle_2019_subset.txt …
Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/PTK2B_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-2968.55924360798” [1] “objective:-2968.40951319364” [1] “objective:-2968.40927818658” [1] “objective:-2968.40927781”

Credible Set:

****** 5 SNPs included in Credible Set ******

## Warning: Removed 1 rows containing missing values (geom_hline).

eQTL

AFR = AFA = African
AMR = Ad Mixed American
EAS = East Asian
EUR = CAU = European
SAS = South Asian
HIS = Hispanic

MESA - AFA

dataset_name <- "MESA_AFA"
superpopulation <- "AFA"
allResults[[dataset_name]] <- finemap_eQTL(superpopulation, gene =  "PTK2B", 
                                         fullSS_path = Data_dirs[[dataset_name]]$fullSS, 
                                         force_new_subset = force_new_subset)

Subset file already exists. Importing Data/eQTL/MESA/PTK2B_AFR_subset.txt …

## Warning in charToRaw(enc2utf8(text)): argument should be a character vector of length 1
## all but the first element will be ignored

PTK2B

Extracting SNPs flanking lead SNP… —Min snp position: 26695121 — —Max snp position: 27695121 — Extracting relevant variants from fullSS…

awk -F " " ‘NR==1{print $0}{ if(($7 == 8) && ($12 >= 26695121 && $12 <= 27695121)) { print } }’ Data/eQTL/MESA/PTK2B_AFR_subset.txt > Data/eQTL/MESA/PTK2B_MESA_subset.txt Extraction completed in 0.09 seconds Calculating Standard Error…

Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/PTK2B_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-4452.41334407999” [1] “objective:-4452.272858439” [1] “objective:-4452.27277053595” [1] “objective:-4452.27277048098”

Credible Set:

****** 2 SNPs included in Credible Set ******

## Warning: Removed 1 rows containing missing values (geom_hline).

MESA - CAU

dataset_name <- "MESA_CAU"
superpopulation <- "CAU"
allResults[[dataset_name]] <- finemap_eQTL(superpopulation, gene = "PTK2B", 
                                         fullSS_path = Data_dirs[[dataset_name]]$fullSS, 
                                         force_new_subset = force_new_subset)

Subset file already exists. Importing Data/eQTL/MESA/PTK2B_EUR_subset.txt …

## Warning in charToRaw(enc2utf8(text)): argument should be a character vector of length 1
## all but the first element will be ignored

PTK2B

Extracting SNPs flanking lead SNP… —Min snp position: 26695121 — —Max snp position: 27695121 — Extracting relevant variants from fullSS…

awk -F " " ‘NR==1{print $0}{ if(($7 == 8) && ($12 >= 26695121 && $12 <= 27695121)) { print } }’ Data/eQTL/MESA/PTK2B_EUR_subset.txt > Data/eQTL/MESA/PTK2B_MESA_subset.txt Extraction completed in 0.06 seconds Calculating Standard Error…

Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/PTK2B_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-2439.61831759579” [1] “objective:-2438.71996233951” [1] “objective:-2438.71907514181” [1] “objective:-2438.71907425949”

Credible Set:

****** 5 SNPs included in Credible Set ******

## Warning: Removed 1 rows containing missing values (geom_hline).

MESA - HIS

Used the Ad Mixed American (AMR) reference panel given the absence of a Hispanic reference panel for 1KG_Phase1.

dataset_name <- "MESA_HIS"
superpopulation <- "HIS"
allResults[[dataset_name]] <- finemap_eQTL(superpopulation, gene = "PTK2B", 
                                       fullSS_path = Data_dirs[[dataset_name]]$fullSS, 
                                       force_new_subset = force_new_subset)

Subset file already exists. Importing Data/eQTL/MESA/PTK2B_AMR_subset.txt …

## Warning in charToRaw(enc2utf8(text)): argument should be a character vector of length 1
## all but the first element will be ignored

PTK2B

Extracting SNPs flanking lead SNP… —Min snp position: 26719987 — —Max snp position: 27719987 — Extracting relevant variants from fullSS…

awk -F " " ‘NR==1{print $0}{ if(($7 == 8) && ($12 >= 26719987 && $12 <= 27719987)) { print } }’ Data/eQTL/MESA/PTK2B_AMR_subset.txt > Data/eQTL/MESA/PTK2B_MESA_subset.txt Extraction completed in 0.08 seconds Calculating Standard Error…

Creating LD matrix… LD Reference Panel = 1KG_Phase1 Creating ‘../1000_Genomes_VCFs’ directory. Identified matching VCF subset file. Importing… ../1000_Genomes_VCFs/Phase1/PTK2B_subset.vcf ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-3444.59629242866” [1] “objective:-3444.46011107977” [1] “objective:-3444.45983730976” [1] “objective:-3444.4598367586”

Credible Set:

— ERROR — ****** Could NOT identify credible set. Default to SNPs with the top 5 PIPs ******

## Warning: Removed 1 rows containing missing values (geom_hline).

Merge Results

merged_results <- merge_finemapping_results(allResults, csv_path ="Results/merged_finemapping_results.csv")
createDT(merged_results, caption = "Finemapped Credible Sets for Multiple Datasets")

## Warning in instance$preRenderHook(instance): It seems your data is too
## big for client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html

# table(as.character(merged_results$SNP))

Fine Mapping:
Parkinson’s Disease
&
Alzheimer’s Disease

Fine Mapping:

Brian M. Schilder, Bioinformatician II
Raj Lab
Department of Neuroscience
Icahn School of Medicine at Mount Sinai
NYC, New York

RAJ LAB

Brian M. Schilder, Bioinformatician II
Raj Lab
Department of Neuroscience
Icahn School of Medicine at Mount Sinai
NYC, New York

Most Recent Update:
2019-04-09

Parkinson’s Disease

Nalls et. al. (2019) w/ 23andMe

LRRK2

SNCA

VPS13C

C5orf24

IP6K2

KCNIP3

GBF1

SCARB2

Alzheimer’s Disease

Posthuma (2018)

PTK2B

Marioni (2018)

PTK2B

Lambert (2013) (IGAP)

PTK2B

Kunkle (2019) (IGAP)

PTK2B

eQTL

MESA - AFA

PTK2B

MESA - CAU

PTK2B

MESA - HIS

PTK2B

Merge Results

Fine Mapping: Parkinson’s Disease&Alzheimer’s Disease

Fine Mapping:

Brian M. Schilder, Bioinformatician II Raj Lab Department of Neuroscience Icahn School of Medicine at Mount Sinai NYC, New York RAJ LAB

Brian M. Schilder, Bioinformatician II Raj Lab Department of Neuroscience Icahn School of Medicine at Mount Sinai NYC, New York

Most Recent Update: 2019-04-09

Parkinson’s Disease

Nalls et. al. (2019) w/ 23andMe

LRRK2

SNCA

VPS13C

C5orf24

IP6K2

KCNIP3

GBF1

SCARB2

Alzheimer’s Disease

Posthuma (2018)

PTK2B

Marioni (2018)

PTK2B

Lambert (2013) (IGAP)

PTK2B

Kunkle (2019) (IGAP)

PTK2B

eQTL

MESA - AFA

PTK2B

MESA - CAU

PTK2B

MESA - HIS

PTK2B

Merge Results

Fine Mapping:
Parkinson’s Disease
&
Alzheimer’s Disease

Brian M. Schilder, Bioinformatician II
Raj Lab
Department of Neuroscience
Icahn School of Medicine at Mount Sinai
NYC, New York

RAJ LAB

Brian M. Schilder, Bioinformatician II
Raj Lab
Department of Neuroscience
Icahn School of Medicine at Mount Sinai
NYC, New York

Most Recent Update:
2019-04-09